Acute kidney injury in pediatric critical care
Bibliographic record
Abstract
Abstract Acute kidney injury (AKI) is a common complication among children experiencing critical illness, and is associated with both short- and long-term morbidity and mortality. In this review, we discuss current evidence for AKI in paediatric critical care including definitions, epidemiology, pathophysiology, risk factors, and strategies for diagnosis, management, and prognosis. Around one in four children admitted to paediatric intensive care units (ICUs) experience AKI, with higher rates among at-risk groups including children with sepsis, malignancy, post-stem cell transplantation, neonates, cardiac and liver disease, and amongst children exposed to nephrotoxic medications. Critically ill children are at risk due to systemic inflammation, microvascular flow alternations, endothelial dysfunction and microthrombi in the context of serious illness. Management is primarily supportive, with up to 5% of critically ill children requiring renal replacement therapy, most often due to pathologic fluid accumulation. Future research priorities include integration of novel biomarkers into routine care for early detection and risk stratification, with a potential role for artificial intelligence. Large-scale, multi-centre prospective studies, including low- and middle-income settings, are needed to improve understanding of risk factors and outcomes for this vulnerable group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".